EDA

Exploratory Data Analysis

The original data was modified to match the region name in the previous map. All Station Names were converted to their region name.

climate_change <- read.csv(file = "Weather Data Bangladesh (1948 - 2013).csv",TRUE, sep = ",", stringsAsFactors = FALSE)

str(climate_change)

## 'data.frame':    21120 obs. of  18 variables:
##  $ SL               : int  0 1 2 3 4 5 6 7 8 9 ...
##  $ region           : chr  "borishal" "borishal" "borishal" "borishal" ...
##  $ YEAR             : int  1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 ...
##  $ Month            : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Max.Temp         : num  29.4 30 28.2 26.6 30 27.8 26.6 29.4 30.1 31.1 ...
##  $ Min.Temp         : num  12.3 14.1 12.3 12.3 13.3 12.7 12.3 14.3 15.1 15.5 ...
##  $ Rainfall         : num  0 0 0 2 10 0 2 17 104 0 ...
##  $ Relative.Humidity: num  68 77 77 77 75 72 77 74 80 77 ...
##  $ Wind.Speed       : num  0.454 0.454 0.454 0.454 0.454 ...
##  $ Cloud.Coverage   : num  0.6 0.8 0.6 1 1.6 0.5 1 0.4 1 1.7 ...
##  $ Bright.Sunshine  : num  7.83 7.83 7.83 7.83 7.83 ...
##  $ Station.Number   : int  41950 41950 41950 41950 41950 41950 41950 41950 41950 41950 ...
##  $ X_COR            : num  536810 536810 536810 536810 536810 ...
##  $ Y_COR            : num  510152 510152 510152 510152 510152 ...
##  $ LATITUDE         : num  22.7 22.7 22.7 22.7 22.7 22.7 22.7 22.7 22.7 22.7 ...
##  $ LONGITUDE        : num  90.4 90.4 90.4 90.4 90.4 ...
##  $ ALT              : int  4 4 4 4 4 4 4 4 4 4 ...
##  $ Period           : num  1949 1950 1951 1952 1953 ...

head(climate_change)

##   SL   region YEAR Month Max.Temp Min.Temp Rainfall Relative.Humidity
## 1  0 borishal 1949     1     29.4     12.3        0                68
## 2  1 borishal 1950     1     30.0     14.1        0                77
## 3  2 borishal 1951     1     28.2     12.3        0                77
## 4  3 borishal 1952     1     26.6     12.3        2                77
## 5  4 borishal 1953     1     30.0     13.3       10                75
## 6  5 borishal 1954     1     27.8     12.7        0                72
##   Wind.Speed Cloud.Coverage Bright.Sunshine Station.Number    X_COR    Y_COR
## 1  0.4537037            0.6        7.831915          41950 536809.8 510151.9
## 2  0.4537037            0.8        7.831915          41950 536809.8 510151.9
## 3  0.4537037            0.6        7.831915          41950 536809.8 510151.9
## 4  0.4537037            1.0        7.831915          41950 536809.8 510151.9
## 5  0.4537037            1.6        7.831915          41950 536809.8 510151.9
## 6  0.4537037            0.5        7.831915          41950 536809.8 510151.9
##   LATITUDE LONGITUDE ALT  Period
## 1     22.7     90.36   4 1949.01
## 2     22.7     90.36   4 1950.01
## 3     22.7     90.36   4 1951.01
## 4     22.7     90.36   4 1952.01
## 5     22.7     90.36   4 1953.01
## 6     22.7     90.36   4 1954.01

knitr::opts_chunk$set(echo = TRUE)

Weather station locatio

Global Temperature Change

Change in RH

From the bar plot, we can see temperature has increases in last few decades, also there is a sharp contrast in humidity change suggesting sudden heavy rain and extreme weather.

library(ggplot2)
library(plotly)

climate_change_chart <- ggplot(climate_change, aes(x = YEAR , y = Relative.Humidity, fill = Max.Temp)) + 
  xlab("Year") +
  ylab("Relative Humidity") +
  theme_minimal(base_size = 14) + scale_fill_viridis_c()

barplot <- climate_change_chart +
  geom_bar( position = "dodge", stat = "identity",color= "white")
ggplotly(barplot)

Change in rainfall intensity

From the bar plot, we can see rainfall intensity has increases in last few decades

climate_change_chart2 <- ggplot(climate_change, aes(x = YEAR ,  y = Rainfall , fill = Max.Temp )) + 
  xlab("Year") +
  ylab("Rainfall") +
  theme_minimal(base_size = 14)+ scale_fill_viridis_c()

barplot2 <- climate_change_chart2 +
  geom_bar( position = "dodge", stat = "identity",color= "white")
ggplotly(barplot2)

Temperature change by region

We can see the increase in temperature was relatively high in Dhaka Division As Dhaka is the capital, it has seen an intense urbanization in recent years aiding to the increased temperature.

library(lubridate)
# adding Year-Month variable as date
climate_change_ymd <- climate_change %>%
  mutate(year_month = ymd(paste(climate_change$YEAR, climate_change$Month, truncated = 1))) 


Min_t1 <- ggplot(climate_change_ymd, aes(year_month, Min.Temp, color = region)) + 
  geom_line() + 
  geom_smooth(se=FALSE, linetype = "dotted") + 
  labs(title = "Minimum Temperature (1949-2013)",
       x = "Year", 
       y = "Minimum Temperature") +
  theme(plot.title = element_text(hjust = 0.5))

ggplotly(Min_t1)

Max_t1 <- ggplot(climate_change_ymd, aes(year_month, Max.Temp, color = region)) + 
  geom_line() + 
  geom_smooth(se=FALSE, linetype = "dotted") + 
  labs(title = "Maximum Temperature (1949-2013)",
       x = "Year", 
       y = "Maximum Temperature") +
  theme(plot.title = element_text(hjust = 0.5))

ggplotly(Max_t1)

knitr::opts_chunk$set(echo = TRUE)